The present invention relates to encoding and decoding data in communications systems and more specifically to communication systems that encode and decode data to account for errors and gaps in communicated data, and to efficiently utilize communicated data emanating from more than one source.
Transmission of files between a sender and a recipient over a communications channel has been the subject of much literature. Preferably, a recipient desires to receive an exact copy of data transmitted over a channel by a sender with some level of certainty. Where the channel does not have perfect fidelity (which covers most all physically realizable systems), one concern is how to deal with data lost or garbled in transmission. Lost data (erasures) are often easier to deal with than garbled data (errors) because the recipient cannot always tell when garbled data is data received in error. Many error correcting codes have been developed to correct for erasures (so called “erasure codes”) and/or for errors (“error-correcting codes”, or “ECC's”). Typically, the particular code used is chosen based on some information about the infidelities of the channel through which the data is being transmitted and the nature of the data being transmitted. For example, where the channel is known to have long periods of infidelity, a burst error code might be best suited for that application. Where only short, infrequent errors are expected a simple parity code might be best.
File transmission between multiple senders and/or multiple receivers over a communications channel has also been the subject of much literature. Typically, file transmission from multiple senders requires coordination among the multiple senders to allow the senders to minimize duplication of efforts. In a typical multiple sender system sending one file to a receiver, if the senders do not coordinate which data they will transmit and when, but instead just send segments of the file, it is likely that a receiver will receive many useless duplicate segments. Similarly, where different receivers join a transmission from a sender at different points in time, a concern is how to ensure that all data the receivers receive from the sender is useful. For example, suppose the sender is continuously transmitting data about the same file. If the sender just sends segments of the original file and some segments are lost, it is likely that a receiver will receive many useless duplicate segments before receiving one copy of each segment in the file.
Another consideration in selecting a code is the protocol used for transmission. In the case of the global internetwork of networks known as the “Internet” (with a capital “I”), a packet protocol is used for data transport. That protocol is called the Internet Protocol or “IP” for short. When a file or other block of data is to be transmitted over an IP network, it is partitioned into equal size input symbols and input symbols are placed into consecutive packets. Being packet-based, a packet oriented coding scheme might be suitable. The “size” of an input symbol can be measured in bits, whether or not the input symbol is actually broken into a bit stream, where an input symbol has a size of M bits when the input symbol is selected from an alphabet of 2M symbols.
The Transport Control Protocol (“TCP”) is a point-to-point packet control scheme in common use that has an acknowledgment mechanism. TCP is good for one-to-one communications, where the sender and recipient both agree on when the transmission will take place and be received and both agree on which transmitters and receivers will be used. However, TCP is often not suitable for one-to-many or many-to-many communications or where the sender and the recipient independently determine when and where they will transmit or receive data.
Using TCP, a sender transmits ordered packets and the recipient acknowledges receipt of each packet. If a packet is lost, no acknowledgment will be sent to the sender and the sender will resend the packet. Packet loss has a number of causes. On the Internet, packet loss often occurs because sporadic congestion causes the buffering mechanism in a router to reach its capacity, forcing it to drop incoming packets. With protocols such as TCP/IP, the acknowledgment paradigm allows packets to be lost without total failure, since lost packets can just be retransmitted, either in response to a lack of acknowledgment or in response to an explicit request from the recipient. Either way, an acknowledgment protocol requires a back channel from the recipient to the sender.
Although acknowledgment-based protocols are generally suitable for many applications and are in fact widely used over the current Internet, they are inefficient, and sometimes completely infeasible, for certain applications. In particular, acknowledgment-based protocols perform poorly in networks with high latencies, high packet loss rates, uncoordinated recipient joins and leaves, and/or highly asymmetric bandwidth. High latency is where acknowledgments take a long time to travel from the recipient back to the sender. High latency may result in the overall time before a retransmission being prohibitively long. High packet loss rates also cause problems where several retransmissions of the same packet may fail to arrive, leading to a long delay to obtain the last one or last few unlucky packets.
“Uncoordinated recipient joins and leaves” refers to the situation where each recipient can join and leave an ongoing transmission session at their own discretion. This situation is typical on the Internet, next-generation services such as “video on demand” and other services to be offered by network providers in the future. In the typical system, if a recipient joins and leaves an ongoing transmission without coordination of the senders, the recipient will likely perceive a loss of large numbers of packets, with widely disparate loss patterns perceived by different recipients.
Asymmetric bandwidth refers to the situation is where a reverse data path from recipient to sender (the back channel) is less available or more costly than the forward path. Asymmetric bandwidth may make it prohibitively slow and/or expensive for the recipient to acknowledge packets frequently and infrequent acknowledgments may again introduce delays.
Furthermore, acknowledgment-based protocols do not scale well to broadcasting, where one sender is sending a file simultaneously to multiple users. For example, suppose a sender is broadcasting a file to multiple recipients over a satellite channel. Each recipient may experience a different pattern of packet loss. Protocols that rely on acknowledgment data (either positive or negative) for reliable delivery of the file require a back channel from each recipient to the sender, and this can be prohibitively expensive to provide. Furthermore, this requires a complex and powerful sender to be able to properly handle all of the acknowledgment data sent from the recipients. Another drawback is that if different recipients lose different sets of packets, rebroadcast of packets missed only by a few of the recipients causes reception of useless duplicate packets by other recipients. Another situation that is not handled well in an acknowledgment-based communication system is where recipients can begin a receiving session asynchronously, i.e., the recipient could begin receiving data in the middle of a transmission session.
Several complex schemes have been suggested to improve the performance of acknowledgment-based schemes, such as TCP/IP for multicast and broadcast. However none has been clearly adopted at this time, for various reasons. For one, acknowledgment-based protocols also do not scale well where one recipient is obtaining information from multiple senders, such as in a low earth orbit (“LEO”) satellite broadcast network. In an LEO network, the LEO satellites pass overhead quickly because of their orbit, so the recipient is only in view of any particular satellite for a short time. To make up for this, the LEO network comprises many satellites and recipients are handed off between satellites as one satellite goes below the horizon and another rises. If an acknowledgment-based protocol were used to ensure reliability, a complex hand-off protocol would probably be required to coordinate acknowledgments returning to the appropriate satellite, as a recipient would often be receiving a packet from one satellite yet be acknowledging that packet to another satellite.
An alternative to an acknowledgment-based protocol that is sometimes used in practice is a carousel-based protocol. A carousel protocol partitions an input file into equal length input symbols, places each input symbol into a packet, and then continually cycles through and transmits all the packets. A major drawback with a carousel-based protocol is that if a recipient misses even one packet, then the recipient has to wait another entire cycle before having a chance at receiving the missed packet. Another way to view this is that a carousel-based protocol can cause a large amount of useless duplicate data reception. For example, if a recipient receives packets from the beginning of the carousel, stops reception for a while, and then starts receiving again at the beginning of the carousel, a large number of useless duplicate packets are received.
One solution that has been proposed to solve the above problems is to avoid the use of an acknowledgment-based protocol, and instead use erasure codes such as Reed-Solomon Codes to increase reliability. One feature of several erasure codes is that, when a file is segmented into input symbols that are sent in packets to the recipient, the recipient can decode the packets to reconstruct the entire file once sufficiently many packets are received, generally regardless of which packets arrive. This property removes the need for acknowledgments at the packet level, since the file can be recovered even if packets are lost. However, many erasure code solutions either fail to solve the problems of acknowledgment-based protocol or raise new problems.
One problem with many erasure codes is that they require excessive computing power or memory to operate. One coding scheme that has been recently developed for communications applications that is somewhat efficient in its use of computing power and memory is the Tornado coding scheme. Tornado codes are similar to Reed-Solomon codes in that an input file is represented by K input symbols and is used to determine N output symbols, where N is fixed before the encoding process begins. Encoding with Tornado codes is generally much faster than encoding with Reed-Solomon codes, as the average number of arithmetic operations required to create the N Tornado output symbols is proportional to N (on the order of tens of assembly code operations times N) and the total number of arithmetic operations required to decode the entire file is also proportional to N.
Tornado codes have speed advantages over Reed-Solomon codes, but with several disadvantages. First, the number of output symbols, N, must be determined in advance of the coding process. This leads to inefficiencies if the loss rate of packets is overestimated, and can lead to failure if the loss rate of packets is underestimated. This is because a Tornado decoder requires a certain number of output symbols (specifically, K+A packets, where A is small compared to K) to decode and restore the original file and if the number of lost packets is greater than N−(K+A), then the original file cannot be restored. This limitation is generally acceptable for many communications problems, so long as N is selected to be greater than K+A by at least the actual packet loss, but this requires an advance guess at the packet loss.
Another disadvantage of Tornado codes is that they require the encoder and decoder to agree in some manner on a graph structure. Tornado codes require a pre-processing stage at the decoder where this graph is constructed, a process that slows the decoding substantially. Furthermore, a graph is specific to a file size, so a new graph needs to be generated for each file size used. Furthermore, the graphs needed by the Tornado codes are complicated to construct, and require different custom settings of parameters for different sized files to obtain the best performance. These graphs are of significant size and require a significant amount of memory for their storage at both the sender and the recipient.
In addition, Tornado codes generate exactly the same output symbol values with respect to a fixed graph and input file. These output symbols consist of the K original input symbols and N−K redundant symbols. Furthermore, N can practically only be a small multiple of K, such as 1.5 or 2 times K. Thus, it is very likely that a recipient obtaining output symbols generated from the same input file using the same graph from more than one sender will receive a large number of useless duplicate output symbols. That is because the N output symbols are fixed ahead of time and are the same N output symbols that are transmitted from each transmitter each time the symbols are sent and are the same N symbols received by a receiver. For example, suppose N=1500, K=1000 and a receiver receives 900 symbols from one satellite before that satellite dips over the horizon. Unless the satellites are coordinated and in sync, the Tornado code symbols received by the receiver from the next satellite might not be additive because that next satellite is transmitting the same N symbols, which is likely to result in the receiver receiving copies of many of the already received 900 symbols before receiving 100 new symbols needed to recover the input file.
Therefore, what is needed is a simple erasure code that does not require excessive computing power or memory at a sender or recipient to implement, and that can be used to efficiently distribute a file in a system with one or more senders and/or one or more recipients without necessarily needing coordination between senders and recipients.